@amnhaclinhmama: Đoạn 1 : Cảm Âm Giấc Mơ Trưa Em nằm em nhớ một ngày trong veo mi do re sol, re sol Do Do một mùa nghiêng nghiêng re sol Re Re Cánh đồng xa mờ MiRe Do Re la cánh cò nghiêng cuối trời la re Do Re sol… Đoạn 2 : Em về nơi ấy, một bờ vai xanh một dòng tóc xanh… mi do re sol, re sol Do Do, re sol DoRe Do Đó là chân trời hay là mưa cuối trời? ReDo la Do la, la re Do Re sol Điệp Khúc Và gió theo em trôi về con đường sol Sol Mi Mi, MiRe Do Re Do Và nắng theo em trên dòng sông vắng sol Sol Mi Mi MiRe Do Re Mi Mùa đã trôi đi trong miền xanh thẳm la Mi Re Re ReĐo la Do re Mùa đã trôi đi những lần em buồn. la Mi Re Re DoReĐo la Do sol Từng dấu chân xưa trên đường em về sol Sol Mi Mi, MiRe Do Re Do Giờ đã ra hoa những cành hoa vắng sol Sol Mi Mi, ReMiRe Do Re Mi Người đã đi qua những lời em kể la Mi Re Re, DoReDo la Do re Này giấc mơ trưa bao giờ em về? la Mi Re Re, ReDo la Do sol Một tiếng chuông chùa. sol Sol Re Do Lời 2 Em nằm em nhớ một ngày trong veo mi do re sol, re sol Do Do một mùa nghiêng nghiêng re sol Re Re Cánh đồng xa mờ MiRe Do Re la cánh cò nghiêng cuối trời la re Do Re sol… Em về nơi ấy, một bờ vai xanh một dòng tóc xanh… mi do re sol, re sol Do Do, re sol DoRe Do Đó là chân trời hay là mưa cuối trời? DoReDo la Do la, la re Do DoRe sol Điệp Khúc 1 Và gió theo em trôi về con đường sol Sol Mi Mi, MiRe Do Re Do Và nắng theo em trên dòng sông vắng sol Sol Mi Mi MiRe Do Re ReMi Mùa đã trôi đi trong miền xanh thẳm la Mi Re Re DoReĐo la Do re Mùa đã trôi đi những lần em buồn. la Mi Re Re DoReĐo la laDo sol Từng dấu chân xưa trên đường em về sol Sol Mi Mi, MiRe Do DoRe Do Giờ đã ra hoa những cành hoa vắng sol Sol Mi Mi, ReMiRe Do Re ReMi Người đã đi qua những lời em kể la Mi Re Re, DoReDo la laDo re Này giấc mơ trưa bao giờ em về? la Mi Re Re, ReDo la laDo sol Một tiếng chuông chùa. sol Sol Re Do… Điệp Khúc 2 Và gió theo em trôi về con đường sol Sol Mi Mi, MiRe Do Re Do Và nắng theo em trên dòng sông vắng sol Sol Mi Mi MiRe Do Re ReMi Mùa đã trôi đi trong miền xanh thẳm la Mi Re Re DoReĐo la laDo re Mùa đã trôi đi những lần em buồn. la Mi Re Re DoReĐo la laDo sol… Từng dấu chân xưa trên đường em về sol Sol Mi Mi, MiRe Do DoRe Do Giờ đã ra hoa những cành hoa vắng sol Sol Mi Mi, ReMiRe Do Re ReMi Người đã đi qua những lời em kể la Mi Re Re, DoReDo la laDo re Này giấc mơ trưa bao giờ em về? la Mi Re Re, ReDo la laDo sol Một tiếng chuông chùa. sol Sol Re Do… Này giấc mơ trưa bao giờ em về? la Mi Re Re, ReDo la laDo sol Một giấc mơ tan… sol Sol Re Mi… #camam #saotruc #amnhac #giacmotrua

ÂM NHẠC DÂN TỘC
ÂM NHẠC DÂN TỘC
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Friday 19 December 2025 09:15:30 GMT
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quanghung99pt
Quanghung19 :
Hay
2025-12-24 12:58:45
1
haitiensinh_88
Hải tiên sinh_88.Thái Nguyên :
🥰🥰🥰
2025-12-21 05:01:59
1
acoustisnvc
Nắng Ấm Xa Dần :
hơi dài tốt thật, hay quá
2025-12-23 15:14:21
1
nah000000009
hihi😝 :
b ơi lèo đó là gì ạ mình hoc piano lèo la dẫm pedal vag còn sáo thi là gì ạ
2025-12-23 16:21:17
1
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➡️ Part 9 of learning ML code from scratch Six weights, one wrong answer, and not one of them knows it was at fault. Backpropagation is how they find out. Two questions guide the video: what even is backpropagation, and why do models need it? Step 1, the forward pass. Two numbers go into a tiny network, run through a hidden layer, and one number comes out. Ours says 0.700. We wanted 1.0, so square the difference and the loss is 0.09. That single number is how wrong the model is. Step 2, the question. Six weights produced that answer together. Which of them is to blame, and by how much? Step 3, the one idea. Change a single weight by a tiny amount and watch the error move. How much the error changes per unit of weight is the gradient of that weight. In the video you can see it happen: the weight wobbles and the error bar answers. Step 4, the shortcut. Every weight running into the same neuron starts from one shared number, the blame of that neuron. Multiply that blame by the value each weight carried and you have its gradient. One blame, computed once, and every weight into that neuron gets its own gradient from it. That is why backpropagation is fast enough to train anything at all. Step 5, backwards. The blame starts at the output and every layer hands its share to the layer in front of it, back through the same network the data came forward through. It goes layer by layer, and inside one layer every neuron is done together. Step 6, the update. The optimizer moves every weight against its own gradient, the whole weight matrix in one go, so no weight waits for its turn. All six gradients here are negative, so all six weights go up. After one step the answer reads 0.769. After sixty it reads 0.968 and the error is down to 0.001. The honest part: every gradient in the video was checked against the measured change of the error. Move that one weight, see what the error does, divide. Backprop and the measurement agree to nine decimal places, otherwise nothing would have rendered. #machinelearning #backpropagation #neuralnetworks #python #coding
➡️ Part 9 of learning ML code from scratch Six weights, one wrong answer, and not one of them knows it was at fault. Backpropagation is how they find out. Two questions guide the video: what even is backpropagation, and why do models need it? Step 1, the forward pass. Two numbers go into a tiny network, run through a hidden layer, and one number comes out. Ours says 0.700. We wanted 1.0, so square the difference and the loss is 0.09. That single number is how wrong the model is. Step 2, the question. Six weights produced that answer together. Which of them is to blame, and by how much? Step 3, the one idea. Change a single weight by a tiny amount and watch the error move. How much the error changes per unit of weight is the gradient of that weight. In the video you can see it happen: the weight wobbles and the error bar answers. Step 4, the shortcut. Every weight running into the same neuron starts from one shared number, the blame of that neuron. Multiply that blame by the value each weight carried and you have its gradient. One blame, computed once, and every weight into that neuron gets its own gradient from it. That is why backpropagation is fast enough to train anything at all. Step 5, backwards. The blame starts at the output and every layer hands its share to the layer in front of it, back through the same network the data came forward through. It goes layer by layer, and inside one layer every neuron is done together. Step 6, the update. The optimizer moves every weight against its own gradient, the whole weight matrix in one go, so no weight waits for its turn. All six gradients here are negative, so all six weights go up. After one step the answer reads 0.769. After sixty it reads 0.968 and the error is down to 0.001. The honest part: every gradient in the video was checked against the measured change of the error. Move that one weight, see what the error does, divide. Backprop and the measurement agree to nine decimal places, otherwise nothing would have rendered. #machinelearning #backpropagation #neuralnetworks #python #coding

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